What is the difference between RPA and intelligent automation?
Robotic process automation replays a recorded sequence: open this screen, copy that field. It is fast and brittle, and it automates only the slice of a process that never varies. Intelligent automation wraps those deterministic steps with AI that handles the variation: a model that reads an invoice in any layout, classifies a complaint, or extracts terms from a contract. The judgment moves into software; the fixed steps stay cheap and deterministic.
What are examples of intelligent automation in practice?
The pattern repeats across functions: document AI that turns unstructured paperwork into structured data, classification that routes each case to the right queue, and generation that drafts the response a person approves. Finance teams run it on invoices and reconciliations, insurers on claims triage, logistics teams on delivery exceptions, as in AI in logistics. The best first candidates are high-volume processes where people copy information between systems and make small, repeatable judgments. Process intelligence tools exist to find them from your own event data.
Where do AI agents fit in intelligent automation?
Agents are the newest layer. Classic intelligent automation decides within one step; an agent can own a whole goal, planning steps and calling systems as tools, which is agentic process automation. The practical difference is autonomy: a pipeline runs stages in fixed order, an agent chooses what to do next. Most companies need both, and the mistake is reaching for an agent where a pipeline with one classification model would do, a trade-off workflow automation covers in more depth.
How do you start without automating a broken process?
Map the process first, then redesign, then automate. Executives who moved early keep repeating the same warning: using AI only to speed up an existing process yields a better version of yesterday (Fortune, read 12 October 2026). Automating a flawed process makes it produce flawed output faster. Pick one process, measure its real cycle time and error rate, redesign the steps that exist only for historical reasons, and only then automate what remains.